Interpretive Approach Research Articles

Interpreting the Paris Agreement in its Normative Environment

Abstract International and regional courts are increasingly being asked to determine the rights and responsibilities of states in relation to climate harm. The context for such requests is that the 2015 Paris Agreement, comprising primarily procedural obligations and obligations of conduct, and premised on national determination, appears by itself radically insufficient to resolve the existential climate crisis the planet is facing. This article explores if this is indeed the case and argues that the 2015 Paris Agreement must be interpreted in its ‘normative environment’. The ‘normative environment’ includes the customary international law principle of harm prevention, other treaties, including human rights ones, and a set of principles of varying legal status but considerable operational relevance and guidance. This article examines specific obligations from the 2015 Paris Agreement and seeks to concretize and strengthen them based on this interpretative approach. While such strengthened obligations might yet be insufficient to decisively resolve the climate crisis, these nevertheless reflect a richer account of international legal resolve relating to climate change—one that is premised on an understanding of international law as a seamless web of inter-locking obligations rather than of treaties functioning as atomistic reflections of state consent in that area.

A Novel Approach for Model Interpretability and Domain Aware Fine-Tuning in AdaBoost

AbstractThe success of machine learning in real-world use cases has increased its demand in mission-critical applications such as autonomous vehicles, healthcare and medical diagnosis, aviation and flight safety, natural disaster prediction, early warning systems, etc. Adaptive Boosting (AdaBoost) is an ensemble learning method that has gained much traction in such applications. Inherently being a non-interpretable model, the interpretability of the AdaBoost algorithm has been a research topic for many years. Furthermore, most of the research being conducted till now is aimed at explaining AdaBoost using perturbation-based techniques. The paper presents a technique to interpret the AdaBoost algorithm from a data perspective using deletion diagnostics and Cook’s distance. The technique achieves interpretability by detecting the most influential data instances and their impact on the feature importance of the model. This interpretability enables domain experts to accurately modify the significance of specific features in a trained AdaBoost model depending on the data instances. Unlike explaining AdaBoost using perturbation-based techniques, interpreting from a data perspective will enable it to debug data-related biases, errors and to impart the knowledge of the domain experts into the model through domain aware fine-tuning. Experimental studies were conducted with diverse real-world multi-feature datasets to demonstrate interpretability and knowledge integration through domain-aware fine-tuning.

INTERPRETING THE CONDITIONS OF BULLYING IN PHOTOBOOK ENTITLED “BULLY”

This study aims to interpret the conditions of bullying as depicted in the photobook "Bully", exploring the symbolic and aesthetic elements used to represent the psychological and emotional dimensions of bullying. Utilizing a qualitative, interpretative approach, the research employs visual analysis and symbolic interpretation to uncover the deeper meanings conveyed through the photobook's imagery. The analysis focuses on how visual elements such as lighting, composition, and perspective reflect the dynamics of power, fear, isolation, and resilience within the context of bullying. The study is grounded in the Social-Ecological Model and Cognitive-Behavioral Theory, which provide frameworks for understanding the multifaceted nature of bullying and the cognitive distortions experienced by both victims and perpetrators. Results reveal that shadows in the images symbolize the psychological distress and cognitive distortions faced by victims and bullies, while light represents hope, resilience, and the potential for cognitive and emotional recovery. The photobook effectively captures the complex interplay between individual experiences and social influences, illustrating how both victims and bullies are affected by and respond to their environments. The findings underscore the importance of addressing both psychological and social factors in bullying interventions and highlight the photobook's role as a powerful medium for visualizing and understanding these dynamics.

An interpretable machine learning approach for predicting the capacity and failure mode of reinforced concrete columns

During seismic events, reinforced concrete (RC) columns play a crucial role in maintaining buildings’ structural integrity. This motivated engineers and practitioners to search for key parameters that influence the load-carrying capacity and failure mechanisms of such columns. However, the complexity and nonlinearity of seismic effects along with the intricate nature of RC columns as a composite system challenge the capabilities of analytical and empirical approaches to accurately capture the response of RC columns. Subsequently, the present study utilizes Machine Learning (ML) techniques to identify the failure modes and predict the corresponding capacities of RC columns based on both their geometrical and material properties. Decision trees and different ensemble methods were employed to predict both the columns’ failure mode and ultimate capacity. A multivariate dataset consisting of 486 cyclically loaded rectangular and circular columns was used to develop and validate the models. In addition, different embedded variable selection techniques were employed to evaluate the significance of input parameters in predicting the performance of columns. Moreover, partial dependence plots and accumulated local effects were employed to uncover the interrelationships between the input features and the modelled outputs. The developed models yielded an average accuracy of 90% and 95% for predicting the failure mode and ultimate capacity of RC columns, respectively. Given such high accuracy, it can be inferred that, ML techniques have the potential to provide efficient and reliable prediction tools to support seismic design and assessment decisions - mitigating seismic risks and empowering resilience planning in the face of extreme events.

Interpretive Structural Modelling and MICMAC Analysis for Rethinking Special Mathematics Education

Quality and inclusive education, especially for people with disabilities, finds priority in academic and policy initiatives for special education (SPED) programs. Several scholars have highlighted the significance of SPED in the domain literature; however, a comprehensive analysis of critical factors contributing to successful teaching, especially mathematics, is currently missing. Hence, the structural relationship among the critical success factors is established in this study through an interpretive structural modeling approach and Matrice d'impacts croisés multiplication appliquée á un classment (MICMAC) analysis. Results from the adopted methodology show that a teacher's mathematical knowledge is the most significant factor contributing to the success of teaching mathematics with SPED students. Allocating resources to improve and address this particular driving factor will influence the remaining factors. The study findings offer valuable insights for decision-makers and relevant stakeholders in academic institutions.

Arisan Dalam Perspektif Akuntansi

This research aims to analyze social gatherings from a accounts receivable accounting perspective, the method in this research is descriptive qualitative interpretive approach. This research will reveal the experiences of research informants regarding social gatherings attended in Padang City. The selection of informants is people who are in the community and have the capability to explain the phenomena they experience. The observation area is determined randomly based on the opportunity the researcher has to carry out observations and interviews and social gathering models that are not found in other areas. Data analysis techniques refers to the Miles, Huberman, and Saldana model. In this research, reduction was carried out simultaneously with data collection so that information was obtained relating to the informant's perspective on arisan activities from a receivables or information perspective. Based on the discussion above, it can be concluded that members' desire to participate in arisan is based on the number of members' desire to save and their awareness of money. collected is closer to recognizing a receivable and if collected early will tend to be considered an investment. Another economic motive is the tendency of some people to use social gatherings as an instrument to obtain investment funds. Some people even use social gatherings as a means of investment by handing over goods or money equivalent to goods with the aim of saving money from the effects of inflation.

Decisions of Women and Society's Elite "Review of Alqur'an Verses (Perspective of Fii Zilalil Qur'an's Interpretation)"

This study aims to explore and analyze the theme of women's deceit and the elite of society presented in the Qur'an, using an interpretive approach from Tafsir Fii Zhilalil Qur'an by Sayyid Qutb. Through this interpretive study, the researcher seeks to understand how the Qur'an describes the character and strategies used by women and the elite of society in the context of deceit, as well as the moral and social implications contained therein. This study uses a qualitative method with a descriptive-analytical approach to examine relevant verses and interpret their meanings in historical, social, and cultural contexts. The results of the study show that the Qur'an acknowledges the existence of women's deceit as a diverse phenomenon, both carried out by women and the elite of society, and teaches humans to recognize and overcome such deceit through wisdom and strong faith.

Exploring machine learning approaches for early diabetes risk prediction: A comprehensive examination of health indicators and models

Background: The increased prevalence of morbidity and mortality associated with Type 2 diabetes is due to changing lifestyles, demanding improved disease management measures. To tackle this, scientists are increasingly looking to technological advances, notably machine learning, for illness prevention and management, particularly in non-communicable diseases. The emphasis is on establishing an early detection system to identify Type 2 diabetes risk factors, enabling prompt treatments and preventative steps to reduce the disease’s rising prevalence. Materials and methods: The research aimed to assess the association of diabetes class with health indicators. Five machine learning models were employed with cross-validation techniques to predict early diabetes risk. The performance matrices of the models were evaluated and compared with the existing work. Results: In multivariate analysis, we found polyuria (β=3.492; Aor=32.872; 95% CI=11.09,97.35; p<0.001), polydipsia (β=-4.100; Aor=60.378; 95%CI=18.28,199.37; p<0.001), polyphagia (β=1.181; Aor=3.25; 95%CI=1.23,8.57; p=0.017), genital thrush (β=1.08; Aor=2.96; 95%CI=1.26,7.53; p=0.023), irritability (β=2.28; Aor=9.82; 95%CI=3.41,28.26; p<0.001), and partial paresis (β=1.2406; Aor=3.45; 95% CI=1.35,8.79; p=0.009) are the potential health risk indicators for positive diabetes class. Conclusion: Using an interpretable feature learning approach for early diabetes prediction improves the use of global health data. This method forecasts hazards correctly and gives insights into influential aspects. As a result, a more proactive healthcare strategy is implemented, allowing for more prompt treatments and encouraging a more hopeful future by improving patient outcomes and lowering the total burden of diabetes on individuals and healthcare systems.

Oncology Healthcare Professionals’ Awareness and Uptake of the Employee Assistance Programme in Three Public Healthcare Facilities in KwaZulu-Natal, South Africa

Institutional psychosocial support services have gained traction, globally, more so within the context of healthcare facilities. This is mainly due to the recognition that institutional psychosocial support services are beneficial to both employees and employers, alike. However, while these institutional psychosocial support services are important, their uptake remains relatively low. With the focus being on the employee assistance programme (EAP), this study aimed to explore the awareness and uptake of institutional psychosocial support services among oncology healthcare professionals (OHPs) in public healthcare facilities in KwaZulu-Natal (KZN), South Africa. Using the interpretative phenomenological approach, the qualitative study was conducted in three public healthcare facilities that offer oncology services in KZN. Semi-structured in-depth interviews were conducted among 31 participants who were OHPs in the three public healthcare facilities. Thematic data analysis produced two main themes and six respective sub-themes. The main themes were awareness of institutional psychosocial support services and low uptake of institutional psychosocial support services, mainly owing to adequate personal support systems, privacy and confidentiality concerns, and stigma. The study findings point to the need for stronger EAPs that are needs-based, promoted regularly, and evaluated, in public healthcare facilities. To encourage service uptake and address privacy, confidentiality and stigma concerns, innovative and non-stigmatising ways of promoting these services are required, including hybrid approaches to the provision of EAP services. The study findings can provide a valuable contribution to the continued development and strengthening of institutional psychosocial support services in public healthcare facilities.

Feature/vector entity retrieval and disambiguation techniques to create a supervised and unsupervised semantic table interpretation approach

Recently, there has been an increasing interest in extracting and annotating tables on the Web. This activity allows the transformation of textual data into machine-readable formats to enable the execution of various artificial intelligence tasks, e.g., semantic search and dataset extension. Semantic Table Interpretation (STI) is the process of annotating elements in a table. The paper explores Semantic Table Interpretation, addressing the challenges of Entity Retrieval and Entity Disambiguation in the context of Knowledge Graphs (KGs). It introduces LamAPI, an Information Retrieval system with string/type-based filtering and s-elBat, an Entity Disambiguation technique that combines heuristic and ML-based approaches. By applying the acquired know-how in the field and extracting algorithms, techniques and components from our previous STI approaches and the state of the art, we have created a new platform capable of annotating any tabular data, ensuring a high level of quality.

“… I carry their stories home …”: experiences of nurses and midwives caring for perinatal adolescent mothers in primary health care settings in Rwanda

IntroductionAdolescent mothers require trauma- and violence-informed care during the perinatal period due to trauma histories and ongoing violence as a result of pregnancy. Nurses and midwives play a critical role in caring for adolescent mothers in primary healthcare settings in Rwanda in the perinatal period.PurposeTo explore the experiences of nurses and midwives working with adolescent mothers in selected primary healthcare settings in Rwanda to inform the delivery of trauma- and violence- informed care.MethodsThis study utilized an interpretive description qualitative approach and was conducted in eight primary healthcare settings in Rwanda. Twelve nurses and midwives working in perinatal services and four heads of health centers participated in in-depth individual interviews. Data were analyzed thematically.ResultsThe analysis revealed four main themes and 11 (sub-themes): (a) relational practice (being creative and flexible, “lending them our ears”); (b) individual challenges of providing care to adolescent mothers (lack of knowledge to provide care related to gender-based violence, and gendered experience); (c) factors contributing to workarounds (inflexible guidelines, lack of protocol and procedures, lack of nurses’ and midwives’ in service training, and the physical structure of the perinatal environment); and (d) vicarious trauma (living the feelings, “I carry their stories home,” and hypervigilance in parenting).ConclusionNurses and midwives find caring for adolescent mothers challenging due to their unique needs. These needs require them to be creative, adaptable, and attentive listeners to better understand their challenges. These practitioners face difficulties such as insufficient specific knowledge related to, for example, gender-based violence, inflexible guidelines, and a lack of protocols and training. Additionally, in the perinatal environment attention to the needs of practitioners in those settings is often lacking, and many nurses and midwives report experiencing vicarious trauma. Consequently, there is a pressing need for guidelines and protocols specifically tailored for the care of adolescent mothers. Ongoing trauma- and violence- informed care training and professional education should be provided to enhance the ability of nurses and midwives to care for adolescent mothers and prevent re-traumatization and mitigate vicarious trauma effectively.

What explains adolescents’ physical activity and sports participation during the COVID-19 pandemic? – an interpretable machine learning approach

ABSTRACT Adolescents’ physical activity (PA) and sports participation declined due to the COVID-19 pandemic. This study aimed to determine the critical socio-ecological factors for PA and sports participation using a machine learning approach. We did a cross-sectional secondary data analysis utilising the 2021 National Survey of Children’s Health (NSCH) dataset (N=16,166; 49.0% female). We applied an interpretable machine learning approach (e.g. decision tree-based models) that examined the critical factors associated with PA and sports participation. The factors related to the intrapersonal, interpersonal, organisational, and community levels of the socio-ecological model. Out of the 25 factors examined, our findings unveiled the 11 critical factors associated with PA and the 10 critical factors associated with sports participation. Factors at the intrapersonal levels (e.g. age, screen time, and race) held greater importance to PA than those at the other three levels. While interpersonal factors (e.g. parent participation in children’s events/activities, family’s highest educational level, and family income level) were most important for sports participation. This study identified that the common critical factors of physical activity and sports participation during the COVID-19 pandemic mainly relied on intrapersonal and interpersonal levels. Unique factors were discussed.

Consumer reconnection through the practice of mindful eating

Consumer society has gradually disconnected consumers from the origin of their consumption, the real needs it truly satisfies, and its social and ecological impact. This research aims to study the phenomenon of consumer reconnection, through which consumers try to experience meaning by becoming mindful of their consumption experiences. We focus specifically on the consumption practice of mindful eating. Using an interpretative approach, we show how the reconnection process unfolds around three dimensions: self, nature, and society. We highlight the tensions related to reconnection in everyday life and examine the coping strategies that emerge to reduce these tensions. Finally, we discuss the implications in terms of education, health, and marketing practices.

Patient Experience of Stiffness With Knee Osteoarthritis: An Interpretative Description Study.

Knee OA (KOA) can lead to pain, loss of muscle strength, and changes in gait. Knee stiffness is a classic feature of KOA that can increase the risk of falls but has been understudied. To evaluate the impact of knee stiffness, the factors influencing the severity of stiffness, and the repercussions on participation for patients with KOA. This qualitative study used an interpretive description approach. Purposeful sampling was used for patients with KOA over 45years of age, fluent in English, diagnosed with KOA and reported KOA stiffness within the last 6months. Participants were recruited through social media and Ontario clinics. Semi-structured interviews were conducted over the phone or using zoom, recorded, and transcribed verbatim. Open, axial, and selective coding were used to identify clinically relevant themes. Twelve participants (5F, 7M) with a mean age of 60years were included. The five themes identified include elusive and variable perceptions of joint stiffness, inactivity or too much activity exacerbates stiffness, adapting to the ebb and flow of symptoms, risk experiences and safety fears leads to reduced participation, and KOA stiffness impairs quality of life. This study highlights characteristics of knee stiffness, consequences on participation, and quality of life for people with KOA. Monitoring knee stiffness for KOA is recommended for more appropriate treatment intensity, which could improve adherence to a home programme and potentially reduce the risk of falls.

‘Their role has a lot of purpose beyond being just being about exercises’: a qualitative study of patients’ experiences of physiotherapy following a mild traumatic brain injury

Following a mild traumatic brain injury (mTBI; also known as concussion), physiotherapists may be involved in injury identification, assessment and rehabilitation. However, how people perceive and experience the physiotherapist’s role...

Childbearing beyond 40: Exploring the psychological state of expectant women through qualitative research

ABSTRACT This study explores the psychological state of middle-aged women who conceived through assisted reproductive technology, focusing on improving their antenatal mental health. The study employed a qualitative research design, utilizing an in-depth interview method to gather data from 16 expectant mothers in middle adulthood. Participants were recruited through purposive sampling applying the maximum variance technique, and data analysis was conducted using an inductive interpretive phenomenology approach. The qualitative analysis yielded 12 subthemes, grouped into two main themes: the psychological consequences and advantages of middle-aged pregnancy. The study revealed that regret, guilt, parental concerns, anticipatory anxiety, past trauma, and health-related anxiety contribute to psychological distress, while dissipation from childlessness, emotional preparedness, and support during life stages positively impact mental health. Antenatal care, prenatal counseling, spousal support, and education may help women mitigate distress and fully enjoy their pregnancy.

Can a Transparent Machine Learning Algorithm Predict Better than Its Black Box Counterparts? A Benchmarking Study Using 110 Data Sets.

We developed a novel machine learning (ML) algorithm with the goal of producing transparent models (i.e., understandable by humans) while also flexibly accounting for nonlinearity and interactions. Our method is based on ranked sparsity, and it allows for flexibility and user control in varying the shade of the opacity of black box machine learning methods. The main tenet of ranked sparsity is that an algorithm should be more skeptical of higher-order polynomials and interactions a priori compared to main effects, and hence, the inclusion of these more complex terms should require a higher level of evidence. In this work, we put our new ranked sparsity algorithm (as implemented in the open source R package, sparseR) to the test in a predictive model "bakeoff" (i.e., a benchmarking study of ML algorithms applied "out of the box", that is, with no special tuning). Algorithms were trained on a large set of simulated and real-world data sets from the Penn Machine Learning Benchmarks database, addressing both regression and binary classification problems. We evaluated the extent to which our human-centered algorithm can attain predictive accuracy that rivals popular black box approaches such as neural networks, random forests, and support vector machines, while also producing more interpretable models. Using out-of-bag error as a meta-outcome, we describe the properties of data sets in which human-centered approaches can perform as well as or better than black box approaches. We found that interpretable approaches predicted optimally or within 5% of the optimal method in most real-world data sets. We provide a more in-depth comparison of the performances of random forests to interpretable methods for several case studies, including exemplars in which algorithms performed similarly, and several cases when interpretable methods underperformed. This work provides a strong rationale for including human-centered transparent algorithms such as ours in predictive modeling applications.

The Impact of Parental Involvement on the Educational Development of Students with Autism Spectrum Disorder.

Parental involvement is vital for the academic success and overall development of students, particularly those with Autism Spectrum Disorder (ASD), who face unique educational challenges. This study investigates the influence of parental involvement on the academic performance and school adjustment of students with ASD, focusing on the interplay of cultural identity and first language in their educational and social growth. The research also seeks to identify the challenges parents face and the strategies they use to support their children. Employing a qualitative, interpretive approach, this study involved 42 parents of secondary school students with ASD in Seville, Spain. Data collection included in-depth interviews and focus group discussions, allowing for a comprehensive understanding of parental experiences. The study found that active parental involvement greatly enhances students' academic performance and emotional well-being. Key strategies include creating structured home environments, utilizing assistive technologies, and fostering open communication with school staff. However, parents reported significant challenges, including limited school resources, communication barriers with educators, and a lack of ASD-specific training for teachers and parents. These challenges often hinder their ability to fully support their children's education. Active parental involvement is crucial for the educational success of students with ASD. The findings highlight the need for improved communication between schools and families, increased resources, and targeted ASD training for both teachers and parents. Additionally, the study advocates for greater investment in assistive technologies to better support students with ASD. Addressing these needs could significantly improve the educational experiences and outcomes for these students.

DINAMIKA GENERASI SANDWICH DALAM PENGELOLAAN KEUANGAN : SEBUAH STUDI FENOMENOLOGI

The purpose of this study is to investigate the significance of the sandwich generation's financial management experiences in challenging financial times. Qualitative research design using interpretive phenomenological approach was employed. Three individuals who embodied the sandwich generation were interviewed in-depth in order to gather data. The Interpretative Phenomenological Analysis method was employed to examine the interview results. The findings of this study suggest that the elements that drive informants to fulfill their role as a sandwich generation include favorable attitudes, subjective norms, perceived behavioral control, and felt religiosity. Based on his interpretation of the event, the informant experienced good emotions such as pride and believed that taking on this responsibility was a way of showing his parents love

Yoga intervention for colorectal cancer survivors: a qualitative study exploring participants’ expectations and experiences

Introduction Colorectal cancer (CRC) survivors often struggle with side effects following treatment such as reduced quality of life, fatigue and psychological distress and need therefore efficient comprehensive interventions. The aim of this qualitative study was to explore CRC survivors’ expectations before the yoga intervention as well as their unique experiences beyond those reported with standard questionnaires. Methods Interpretative phenomenological approach was used in this qualitative study. Semi-structured interviews were conducted before and after a 10-week yoga program (90 min once a week, Hatha Yoga) with CRC survivors enrolled in a randomized controlled trial. Thematic analysis was used to uncover themes present in participants’ accounts. Results Nine patients participated in the interviews, mean interview duration was 27.49 min (SD = 7.71) before and 38.41 min (SD = 15.93) after the intervention. Our analysis identified following themes: (1) representations and expectations from the yoga intervention; (2) course structure and implementation; (3) perceptions and effects of the intervention; (4) differences between the study yoga intervention and other physical activities. The superordinate theme regarding effects of intervention included aspects of intervention at multiple levels such as emotional, physical, behavioral and spiritual. Conclusions This qualitative study provides valuable insight regarding CRC survivors’ expectations and experiences following a 10-week yoga intervention. While expectations varied from skepticism to specific symptom improvement, the majority of participants had a positive, open attitude towards yoga. Consistent with participants’ experiences, yoga may represent a promising intervention for CRC survivors if the groups’ specific concerns are taken into account.